AI Research Engineer; Kernel & Inference Optimization

Join Tether and Shape the Future of Digital Finance

At Tether, we’re not just building products, we’re pioneering a global financial revolution. Our cutting‑edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve‑backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction.

As a member of our AI model team, you will drive innovation in model serving and inference architectures for advanced AI systems. Your work will focus on optimizing model deployment and inference strategies to deliver highly responsive, efficient, and scalable performance across real‑world applications. You will work on a wide spectrum of systems, ranging from resource‑efficient models designed for limited hardware environments to complex, multi‑modal architectures that integrate data such as text, images, and audio.

We expect you to have deep expertise in designing and optimizing model serving pipelines and inference frameworks as well as a strong background in advanced model architectures. You will adopt a hands‑on, research‑driven approach to develop, test, and implement novel serving strategies and inference algorithms. Your responsibilities include engineering robust inference pipelines, establishing comprehensive performance metrics, and identifying and resolving bottlenecks in production environments.

The ultimate goal is to enable high‑throughput, low‑latency, low‑memory footprint, and scalable AI performance that delivers tangible value in dynamic, real‑world scenarios.

• Design and deploy state‑of‑the‑art model serving architectures that deliver high throughput and low latency while optimizing memory usage. Ensure these pipelines run efficiently across diverse environments, including resource‑constrained devices and edge platforms. Establish clear performance targets such as reduced latency, improved token response, and minimized memory footprint.
• Build, run, and monitor controlled inference tests in both simulated and live production environments. Track key performance indicators such as response latency, throughput, memory consumption, and error rates, with special attention to metrics specific to resource‑constrained devices. Document iterative results and compare outcomes against established benchmarks to validate performance across platforms.
• Identify and prepare high‑quality test datasets and simulation scenarios tailored to real‑world deployment challenges, specifically those encountered on low‑resource devices. Set measurable criteria to ensure that these resources effectively evaluate model performance, latency, and memory utilization under various operational conditions.
• Analyze computational efficiency and diagnose bottlenecks in the serving pipeline by monitoring both processing and memory metrics. Address issues such as suboptimal batch processing, network delays, and high memory usage to optimize the serving infrastructure for scalability and reliability on resource‑constrained systems.
• Work closely with cross‑functional teams to integrate optimized serving and inference frameworks into production pipelines designed for edge and on‑device applications. Define clear success metrics such as improved real‑world performance, low error rates, robust scalability, optimal memory usage and ensure continuous monitoring and iterative refinements for sustained improvements.

• A degree in Computer Science or related field. Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A
  * conferences).
• Must have knowledge of Metal Shading Language (MSL). You should be comfortable writing custom compute shaders from scratch.
• Proven experience in low‑level kernel optimizations and inference optimization on mobile devices is essential. Your contributions should have led to measurable improvements in inference latency,…